Application Security Engineer

Anthropic Anthropic · AI Frontier · San Francisco, CA · Security

Application Security Engineer role focused on building security into the SDLC for AI products and internal tools. Responsibilities include threat modeling, secure design reviews, developing security tooling, managing vulnerability and bug bounty programs, and collaborating with engineers and researchers to instill security best practices. Requires experience in application and infrastructure security, cloud environments, and a developer mindset. Familiarity with AI/ML security risks is a plus.

What you'd actually do

  1. Help secure AI products and internal tools that are introducing industry-novel security risks and pushing established security boundaries
  2. Lead “shift left” security efforts to build security into the software development lifecycle.
  3. Conduct secure design reviews and threat modeling. Identify and prioritize risks, attack surfaces, and vulnerabilities.
  4. Develop tooling to scale security code reviews and respond to developer questions, including advising developers on remediating vulnerabilities and following secure coding practices.
  5. Manage Anthropic's vulnerability management program, including integrating data ingestion pipelines, coding logic to prioritize vulnerability fixes, supporting teams remediating vulnerabilities and developing automated systems at scale.

Skills

Required

  • Application security
  • Infrastructure security
  • Cloud-based environments
  • Containerized environments
  • Python
  • Rust
  • Go
  • Java
  • Threat modeling
  • Secure design reviews
  • Vulnerability management
  • Bug bounty program management
  • Secure coding practices
  • Adversary perspective

Nice to have

  • Kubernetes
  • Docker
  • AWS
  • GCP
  • Offensive security techniques
  • Vulnerability testing
  • Pen testing
  • Red team exercises
  • AI/ML security risks
  • Prompt injection
  • Data poisoning
  • Model extraction
  • Building security tools
  • Automated tools
  • Software engineering principles
  • Security engineering principles
  • Fast-paced environments
  • Navigating ambiguity

What the JD emphasized

  • security is a core consideration
  • security risks
  • secure coding best practices
  • emerging threats to AI/ML
  • security champions
  • think like an attacker
  • developer mindset
  • hands-on experience in application and infrastructure security
  • securing cloud-based and containerized environments
  • offensive security
  • AI/ML security risks
  • prompt injection
  • data poisoning
  • model extraction